CN108376997B - Active power distribution network island division method considering distributed power supply uncertainty - Google Patents

Active power distribution network island division method considering distributed power supply uncertainty Download PDF

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CN108376997B
CN108376997B CN201810226467.6A CN201810226467A CN108376997B CN 108376997 B CN108376997 B CN 108376997B CN 201810226467 A CN201810226467 A CN 201810226467A CN 108376997 B CN108376997 B CN 108376997B
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distribution network
active power
power supply
node
power
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CN108376997A (en
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王成山
冀浩然
李鹏
宋关羽
赵金利
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Tianjin University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network

Abstract

An active power distribution network island division method considering distributed power supply uncertainty comprises the following steps: inputting the structure and parameters of the active power distribution network according to the selected active power distribution network; establishing a deterministic active power distribution network island division model according to the active power distribution network structure and parameters, and obtaining a compact form of the active power distribution network island division model; according to the compact form of the island division model of the active power distribution network, an uncertain set of uncontrollable distributed power supplies is set, and an island division robust model of the active power distribution network considering the uncertainty of the distributed power supplies is established; solving the active power distribution network island division robust model by adopting a column and constraint generation algorithm; and outputting a solving result, wherein the solving result comprises the recovery load coefficient of each node, the output power and the control mode of the controllable/uncontrollable distributed power supply, the charge-discharge power and the charge state of the energy storage system and the transmission power of the intelligent soft switch. The invention can effectively improve the load recovery level, thereby further improving the power supply reliability of the active power distribution network.

Description

Active power distribution network island division method considering distributed power supply uncertainty
Technical Field
The invention relates to an active power distribution network island division method. In particular to an active power distribution network island division method considering distributed power supply uncertainty.
Background
Reliability is one of important characteristics of an active power distribution network, and especially, islanding operation of part of important loads can be realized by using Distributed Generators (DG) under extreme fault conditions, so as to improve the reliability of system operation. After a power distribution system fails, the coordinated cooperation of a distributed power supply and an Energy Storage System (ESS) can often be used to ensure that important loads are continuously supplied with power, so that the range of affected users is reduced, and the reliability of the system is improved. The energy storage system can effectively solve the problem of efficient utilization of the high-permeability distributed power supply through the transfer of energy in time, and further optimizes the running state of the power distribution system. Under the condition of a power distribution system fault, important loads are guaranteed to be supplied continuously through coordination of the distributed power supply and the energy storage system, the range of affected users is reduced, and the method plays an important role in improving the reliability of the system.
The intelligent soft Switch (SOP) is a novel intelligent power distribution device replacing a traditional interconnection switch, the flexibility and the controllability of the operation of a power distribution system are greatly improved by the application of the intelligent soft switch, compared with the interconnection switch, the power control of the intelligent soft switch is safer and more reliable, the continuous adjustment of power can be realized, and the potential safety hazard possibly brought by the switch operation is avoided. In the island operation process, the intelligent soft switch can provide effective voltage support, so that the load recovery level is improved. The initial research is carried out on the existing scholars at home and abroad, but the effect of the intelligent soft switch in the load recovery process is less involved. And when the distribution network is operated in an isolated island mode, because the output of distributed power supplies such as photovoltaic power supplies and fans has strong randomness, when the factors are not considered, the load recovery level of the distribution network is often low. The operation strategy of SOP is more required to have the capability of coping with uncertainty, namely certain robustness. Therefore, an active power distribution network islanding method capable of fully considering the uncertainty of the distributed power supply is needed to solve the islanding problem of the active power distribution network under the condition of considering the uncertainty of the distributed power supply.
Disclosure of Invention
The invention aims to solve the technical problem of providing an active power distribution network island division method considering distributed power uncertainty by comprehensively considering power distribution network operation constraint, controllable and uncontrollable distributed power supply operation constraint, energy storage system operation constraint and intelligent soft switch operation constraint.
The technical scheme adopted by the invention is as follows: an active power distribution network island division method considering distributed power supply uncertainty comprises the following steps:
1) according to the selected active power distribution network, inputting line parameters, load levels, network topology connection relations, access positions and capacities of a controllable/uncontrollable distributed power supply, an energy storage system and an intelligent soft switch, operation curves of the load and the uncontrollable distributed power supply, system fault positions and island duration, reference voltage and reference power initial values;
2) establishing a deterministic active power distribution network island division model according to the active power distribution network structure and parameters provided in the step 1), and obtaining a compact form of the active power distribution network island division model, wherein the compact form comprises the following steps: setting the maximum restoration active load capacity of an active power distribution network within a period of time as an objective function, and respectively considering island generation constraint, power distribution network operation constraint, controllable/uncontrollable distributed power supply operation constraint, energy storage system operation constraint and intelligent soft switch operation constraint;
3) setting an uncertain set of the uncontrollable distributed power sources according to the compact form of the active power distribution network island division model obtained in the step 2), and establishing an active power distribution network island division robust model considering the uncertainty of the distributed power sources;
4) solving the active power distribution network island division robust model obtained in the step 3) by adopting a column and constraint generation algorithm;
5) and outputting the solving result of the step 4), including the recovery load coefficient of each node, the output power and the control mode of the controllable/uncontrollable distributed power supply, the charging and discharging power and the charge state of the energy storage system and the transmission power of the intelligent soft switch.
The compact form of the island division model of the active power distribution network in the step 2) is expressed as
Figure GDA0002780042230000021
s.t.Ax+Dy≥f
Bx+Cy＝d0
‖Hx+Gy‖2≤hTx+gTy
In the formula, variable
Figure GDA0002780042230000022
Representing the operation strategies of the controllable/uncontrollable distributed power supply, the energy storage system and the intelligent soft switch;
Figure GDA0002780042230000023
respectively the active power and the reactive power injected by the controllable distributed power supply on the node i in the t period;
Figure GDA0002780042230000024
respectively, is a period of tActive power and reactive power injected by the uncontrollable distributed power supply on the node i;
Figure GDA0002780042230000025
the charging and discharging power of the energy storage system at the node i in the t period is positive, and the charging power is negative;
Figure GDA0002780042230000026
injecting reactive power into the energy storage system at a node i in the t period;
Figure GDA0002780042230000027
active power and reactive power injected by intelligent soft switches connected to the nodes i and j in the time period t respectively; the variable y: is ═ U (U)t,i,It,ji,Pt,ji,Qt,ji,Pt,i,Qt,i)TRepresenting a power flow control variable; pt,ji、Qt,jiRespectively the active power and the reactive power flowing through the branch ji at the time period t; pt,i、Qt,iRespectively the sum of active power and reactive power injected into the node i in the t period; i ist,jiThe amplitude of the current flowing through the branch ji at the time t; u shapet,iThe voltage amplitude of the node i is t time period; the symbol A, B, C, D, H, G is the coefficient matrix of the model, and b, c, f, g, h are the coefficient vectors of the model;
Figure GDA0002780042230000028
representing the active power contribution prediction value of the uncontrollable distributed power supply, wherein,
Figure GDA0002780042230000029
and the active output predicted value of the uncontrollable distributed power supply on the node i in the t period is obtained.
The uncertain set of the uncontrollable distributed power supply in the step 3) is expressed as
Figure GDA00027800422300000210
In the formula (I), the compound is shown in the specification,
Figure GDA00027800422300000211
representing a set of uncertainties for the uncontrollable distributed power supply; d represents the uncertainty active power output of the uncontrollable distributed power supply;
Figure GDA00027800422300000212
active power injected into the uncontrollable distributed power supply on the node i in the t period;
Figure GDA00027800422300000213
the active output predicted value of the uncontrollable distributed power supply on the node i in the t period is obtained; omeganThe method comprises the steps of collecting all nodes of a source power distribution network;
Figure GDA00027800422300000214
the fluctuation deviation of the active power output of the uncontrollable distributed power supply on a node i in the t period is obtained; gamma-shapedNDGParameters are adjusted for uncertainty of the uncontrollable distributed power supply.
The active power distribution network islanding robust model considering the uncertainty of the distributed power supply in the step 3):
Figure GDA00027800422300000215
s.t.
Figure GDA00027800422300000216
wherein
Figure GDA00027800422300000217
Figure GDA0002780042230000031
In the formula, variable
Figure GDA0002780042230000032
Representing the operation strategies of the controllable/uncontrollable distributed power supply, the energy storage system and the intelligent soft switch;
Figure GDA0002780042230000033
respectively the active power and the reactive power injected by the controllable distributed power supply on the node i in the t period;
Figure GDA0002780042230000034
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the node i in the t period;
Figure GDA0002780042230000035
the charging and discharging power of the energy storage system at the node i in the t period is positive, and the charging power is negative;
Figure GDA0002780042230000036
injecting reactive power into the energy storage system at a node i in the t period;
Figure GDA0002780042230000037
active power and reactive power injected by intelligent soft switches connected to the nodes i and j in the time period t respectively; the variable y: is ═ U (U)t,i,It,ji,Pt,ji,Qt,ji,Pt，i，Qt，i)TRepresenting a power flow control variable; pt，ji、Qt，jiRespectively the active power and the reactive power flowing through the branch ji at the time period t; pt，i、Qt，iRespectively the sum of active power and reactive power injected into the node i in the t period; i ist，jiThe amplitude of the current flowing through the branch ji at the time t; u shapet，iThe voltage amplitude of the node i is t time period; the symbol A, B, C, D, H, G is a coefficient matrix of the model, and c, f, g, h are coefficient vectors of the model; d represents the uncertainty active power output of the uncontrollable distributed power supply.
The invention discloses an active power distribution network island division method considering distributed power supply uncertainty, which aims to solve the problem of active power distribution network island operation, fully considers randomness and volatility of distributed power supplies, establishes an active power distribution network island division robust model considering the distributed power supply uncertainty, and adopts a column and constraint generation algorithm to solve to obtain an active power distribution network island division strategy. The method can effectively improve the load recovery level, thereby further improving the power supply reliability of the active power distribution network.
Drawings
FIG. 1 is a flow chart of an active power distribution network islanding method of the invention considering distributed power source uncertainty;
FIG. 2 is a diagram of an improved IEEE33 node algorithm;
FIG. 3 is a photovoltaic system and load operating curve;
FIG. 4 is a scheme 1 islanding result;
FIG. 5 is a scheme 2 islanding result;
FIG. 6a shows charging and discharging power of the energy storage system at each period;
FIG. 6b is a state of charge value for each period of the energy storage system;
fig. 7a is the value of the active power transmitted by the intelligent soft switch 1;
fig. 7b is the value of the active power transmitted by the intelligent soft switch 2;
fig. 8a is the value of the reactive power emitted by the intelligent soft switch 1;
fig. 8b is the value of the reactive power issued by the intelligent soft switch 2;
FIG. 9a is the active power of a controllable distributed power supply;
fig. 9b is a value of reactive power of the controllable distributed power supply.
Detailed Description
The detailed description of the active power distribution network islanding method considering distributed power supply uncertainty is described below with reference to embodiments and drawings.
As shown in fig. 1, an active power distribution network islanding method considering uncertainty of a distributed power supply according to the present invention includes the following steps:
1) according to the selected active power distribution network, inputting line parameters, load levels, network topology connection relations, access positions and capacities of a controllable/uncontrollable distributed power supply, an energy storage system and an intelligent soft switch, operation curves of the load and the uncontrollable distributed power supply, system fault positions and island duration, reference voltage and reference power initial values;
2) establishing a deterministic active power distribution network island division model according to the active power distribution network structure and parameters provided in the step 1), and obtaining a compact form of the active power distribution network island division model, wherein the compact form comprises the following steps: setting the maximum restoration active load capacity of an active power distribution network within a period of time as an objective function, and respectively considering island generation constraint, power distribution network operation constraint, controllable/uncontrollable distributed power supply operation constraint, energy storage system operation constraint and intelligent soft switch operation constraint; wherein the content of the first and second substances,
(1) the maximum active load recovery amount of the active power distribution network in a period of time is expressed as an objective function
Figure GDA0002780042230000041
In the formula, omegatThe method comprises the following steps of (1) collecting the island operation time of an active power distribution network; omeganThe method comprises the steps of collecting all nodes of a source power distribution network; lambda [ alpha ]iFor the recovery of the load on node i, λi∈{0，1}，λ i1 denotes the load recovery on node i, λ i0 indicates that the load on node i is not recovered;
Figure GDA0002780042230000042
is the active load on node i during time t.
(2) The island generation constraint is expressed as
αij＝βij+βji,ij∈Ωb (2)
Figure GDA0002780042230000043
Figure GDA0002780042230000044
αij∈{0，1} (5)
0≤βij≤1，0≤βji≤1 (6)
Figure GDA0002780042230000045
In the formula, omegabRepresenting a collection of all branches of the power distribution system; omeganThe method comprises the steps of collecting all nodes of a source power distribution network; omegasThe node set is used for providing voltage and frequency support when the power distribution system operates in an isolated island mode; omegan\ΩsRepresents omeganInternal omega removalsAll but nodes; alpha is alphaijRepresenting the open state of the switch on branch ij, alpha ij1 denotes switch closed, α ij0 indicates that the switch is open; beta is aijRepresents the relationship of node i and node j, β ij1 means that node j is the parent node of node i, otherwise βij＝0；Ut，iIs the voltage amplitude, U, of node i during a period t0For a given voltage base value, M represents a set constant.
When the controllable distributed power supply, the energy storage device or the intelligent soft switch on the node i meets the requirement
Figure GDA0002780042230000046
βijWhen the value is 1, selecting a PQ control mode; when the controllable distributed power supply, the energy storage device or the intelligent soft switch on the node i meets the requirement
Figure GDA0002780042230000047
βijWhen the value is equal to 0, a Vf control mode is selected.
(3) The power distribution network operation constraint is represented as:
Figure GDA0002780042230000048
Figure GDA0002780042230000049
Figure GDA00027800422300000410
Figure GDA00027800422300000411
Figure GDA00027800422300000412
Figure GDA00027800422300000413
Figure GDA0002780042230000051
-Mαij≤Pt，ji≤Mαij (15)
-Mαij≤Pt，ji≤Mαij (16)
Figure GDA0002780042230000052
in the formula, omegabRepresenting a collection of all branches of the power distribution system; pt，ji、Qt，jiRespectively the active power and the reactive power flowing through the branch ji at the time period t; pt，i、Qt，iRespectively the sum of active power and reactive power injected into the node i in the t period; i ist，jiThe amplitude of the current flowing through the branch ji at the time t; u shapet，iThe voltage amplitude of the node i is t time period; u shapet，jThe voltage amplitude of node j is the t period; rjiIs the resistance of branch ji, XjiIs the reactance of branch ji;
Figure GDA0002780042230000053
respectively, is a period of tActive power and reactive power injected by the uncontrollable distributed power supply on the node i;
Figure GDA0002780042230000054
respectively the active power and the reactive power injected by the controllable distributed power supply on the node i in the t period;
Figure GDA0002780042230000055
respectively the active power and the reactive power injected by the energy storage system at the node i in the t period;
Figure GDA0002780042230000056
respectively injecting active power and reactive power into the intelligent soft switch at the node i in the t period; lambda [ alpha ]iFor the recovery of the load on node i, λi∈ {0，1}，λi1 denotes the load recovery on node i, λi0 indicates that the load on node i is not recovered;
Figure GDA0002780042230000057
respectively the active power and the reactive power consumed by the load on the node i in the t period; alpha is alphaijRepresenting the open state of the switch on branch ij, alphaij1 denotes switch closed, αij0 indicates that the switch is open; m represents a set constant;
(4) the safety operation constraint of the power distribution network is expressed as
Figure GDA0002780042230000058
Figure GDA0002780042230000059
In the formula (I), the compound is shown in the specification,
Figure GDA00027800422300000510
and
Figure GDA00027800422300000511
are respectively node iUpper and lower limits of voltage amplitude; u shapet，iThe voltage amplitude of the node i is t time period;
Figure GDA00027800422300000512
is the current amplitude upper limit of branch ji; i ist，jiThe magnitude of the current flowing in branch ji is the t period.
(5) The uncontrollable distributed power supply operation constraint is expressed as
Figure GDA00027800422300000513
Figure GDA00027800422300000514
Figure GDA00027800422300000515
In the formula (I), the compound is shown in the specification,
Figure GDA00027800422300000516
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the node i in the t period;
Figure GDA00027800422300000517
the active output predicted value of the uncontrollable distributed power supply on the node i in the t period is obtained;
Figure GDA00027800422300000518
representing the capacity of the uncontrollable distributed power supply on the node i;
Figure GDA00027800422300000519
the minimum power factor that allows operation for the distributed power supply on node i.
(6) The controllable distributed power supply operation constraint is expressed as
Figure GDA00027800422300000520
Figure GDA00027800422300000521
Figure GDA00027800422300000522
In the formula (I), the compound is shown in the specification,
Figure GDA00027800422300000523
respectively the active power and the reactive power injected by the controllable distributed power supply on the node i in the t period;
Figure GDA00027800422300000524
the active output upper and lower limits of the controllable distributed power supply on the node i are set;
Figure GDA00027800422300000525
representing the capacity of the controllable distributed power supply on the node i;
Figure GDA00027800422300000526
the minimum power factor that allows operation for the distributed power supply on node i.
(7) The energy storage system operation constraint is expressed as
Figure GDA0002780042230000061
Figure GDA0002780042230000062
Figure GDA0002780042230000063
Figure GDA0002780042230000064
Figure GDA0002780042230000065
In the formula (I), the compound is shown in the specification,
Figure GDA0002780042230000066
the charging and discharging power of the energy storage system at the node i in the t period is positive, and the charging power is negative;
Figure GDA0002780042230000067
injecting reactive power into the energy storage system at a node i in the t period;
Figure GDA0002780042230000068
the capacity of the energy storage system on the node i;
Figure GDA0002780042230000069
the upper limit of the reactive power of the energy storage system on the node i is set;
Figure GDA00027800422300000610
the state of charge of the energy storage system on a node i at the initial time of the t period; delta t is an optimized calculation step length;
Figure GDA00027800422300000611
the loss of the energy storage system on the node i in the period t;
Figure GDA00027800422300000612
the loss coefficient of the energy storage system on the node i is obtained;
Figure GDA00027800422300000613
and
Figure GDA00027800422300000614
respectively the upper and lower limits of the state of charge of the energy storage system on the node i.
(8) The intelligent soft switch operation constraint is expressed as
Figure GDA00027800422300000615
Figure GDA00027800422300000616
Figure GDA00027800422300000617
Figure GDA00027800422300000618
Figure GDA00027800422300000619
In the formula (I), the compound is shown in the specification,
Figure GDA00027800422300000620
active power and reactive power injected by intelligent soft switches connected to the nodes i and j in the time period t respectively;
Figure GDA00027800422300000621
and
Figure GDA00027800422300000622
the loss factor of the intelligent soft switch;
Figure GDA00027800422300000623
and
Figure GDA00027800422300000624
the current converter losses connected to the nodes i and j at the time period t respectively;
Figure GDA00027800422300000625
and
Figure GDA00027800422300000626
the converter capacities connected to nodes i and j, respectively.
(9) The compact form of the island division model of the active power distribution network is expressed as
Figure GDA00027800422300000627
s.t.Ax+Dy≥f (37)
Bx+Cy＝d0 (38)
‖Hx+Gy‖2≤hTx+gTy (39)
In the formula, variable
Figure GDA00027800422300000628
Representing the operation strategies of the controllable/uncontrollable distributed power supply, the energy storage system and the intelligent soft switch; the symbol A, B, C, D, H, G is the coefficient matrix of the model, and b, c, f, g, h are the coefficient vectors of the model;
Figure GDA00027800422300000629
and representing the active output predicted value of the uncontrollable distributed power supply.
3) Setting an uncertain set of the uncontrollable distributed power sources according to the compact form of the active power distribution network island division model obtained in the step 2), and establishing an active power distribution network island division robust model considering the uncertainty of the distributed power sources; wherein
(1) The uncertain set of the uncontrollable distributed power supply is expressed as
Figure GDA0002780042230000071
In the formula (I), the compound is shown in the specification,
Figure GDA0002780042230000072
representing a set of uncertainties for the uncontrollable distributed power supply; d represents the uncertainty active power output of the uncontrollable distributed power supply;
Figure GDA0002780042230000073
active power injected into the uncontrollable distributed power supply on the node i in the t period;
Figure GDA0002780042230000074
the active output predicted value of the uncontrollable distributed power supply on the node i in the t period is obtained; omeganThe method comprises the steps of collecting all nodes of a source power distribution network;
Figure GDA0002780042230000075
the fluctuation deviation of the active power output of the uncontrollable distributed power supply on a node i in the t period is obtained; gamma-shapedNDGParameters are adjusted for uncertainty of the uncontrollable distributed power supply.
(2) The island division robust model of the active power distribution network considering the uncertainty of the distributed power supply comprises the following steps:
Figure GDA0002780042230000076
Figure GDA0002780042230000077
wherein
Figure GDA0002780042230000078
Figure GDA0002780042230000079
In the formula, variable
Figure GDA00027800422300000710
Representing the operation strategies of the controllable/uncontrollable distributed power supply, the energy storage system and the intelligent soft switch; the symbol A, B, C, D, H, G is a coefficient matrix of the model, and c, f, g, h are coefficient vectors of the model; d represents the uncertainty active power output of the uncontrollable distributed power supply.
4) Solving the active power distribution network island division robust model obtained in the step 3) by adopting a column-and-constraint generation algorithm (C & CG);
5) and outputting the solving result of the step 4), including the recovery load coefficient of each node, the output power and the control mode of the controllable/uncontrollable distributed power supply, the charging and discharging power and the charge state of the energy storage system and the transmission power of the intelligent soft switch.
Specific examples are given below:
for the present embodiment, first, the impedance value of the line element in the IEEE33 node system, the active power and the reactive power of the load element, and the network topology connection relationship are input, the example structure is shown in fig. 2, and the detailed parameters are shown in tables 1 and 2; 4 groups of controllable distributed power supplies are accessed to the nodes 13, 20, 23 and 30, the capacity is 400kVA, and the minimum power factor is 0.9; 5 groups of uncontrollable distributed power supplies are accessed to the nodes 5, 8, 17, 27 and 33, a photovoltaic power generation system is selected in the embodiment, the capacity is 300kVA, and the minimum power factor is 0.9; the access position and parameters of the energy storage system are shown in a table 3; two groups of intelligent soft switch SOP access test examples are set to replace tie lines 35 and 36, the direction of injecting power into the system is specified to be a positive direction, and specific parameters are shown in a table 4; the operating curves of the photovoltaic power generation system and the load are shown in fig. 3; assume that branches 1-2 are at 6: 00, a permanent three-phase fault occurs, after fault isolation, the loads from the node 2 to the node 33 are all lost of power, and the island operates for 4 hours; finally, the reference voltage of the system is set to 12.66kV, and the reference power is set to 1 MVA.
A robust island division method and a deterministic island division method are adopted for comparative analysis, the uncertainty of a distributed power supply is considered in scheme 1, the robust island division of an active power distribution network is carried out by adopting the distributed power supply, an energy storage and an intelligent soft switch, and the deterministic island division of the active power distribution network is carried out by adopting the distributed power supply, the energy storage and the intelligent soft switch in scheme 2. The islanding results for schemes 1 and 2 are shown in fig. 4 and 5. In the scheme 1, the operation strategy of the energy storage system is shown in fig. 6a and 6b, the operation strategy of the intelligent soft switch is shown in fig. 7a and 7b and fig. 8a and 8b, and the operation strategy of the controllable distributed power supply is shown in fig. 9a and 9 b. Monte Carlo simulation tests are carried out 500 times to find the worst scene, and based on the two schemes, the test results are shown in Table 5.
The computer hardware environment for executing the optimization calculation is Intel (R) Xeon (R) CPU E5-1620, the main frequency is 3.70GHz, and the memory is 32 GB; the software environment is a Windows 10 operating system.
Comparing the scheme 1 with the scheme 2, it can be seen that when uncertainty of photovoltaic output is considered, the robust island division method of the active power distribution network can still effectively improve the load recovery level, so that the power supply reliability of the active power distribution network is further improved.
TABLE 1 IEEE33 node sample load access location and Power
Figure GDA0002780042230000081
TABLE 2 IEEE33 node exemplary line parameters
Figure GDA0002780042230000082
Figure GDA0002780042230000091
TABLE 3 energy storage System configuration
Figure GDA0002780042230000092
TABLE 4 SOP configuration case
Position of Coefficient of loss Rated capacity/kVA
12-22 0.02 800
18-33 0.02 800
Table 5 results of different protocol tests
Figure GDA0002780042230000093

Claims (1)

1. An active power distribution network island division method considering distributed power supply uncertainty is characterized by comprising the following steps:
1) according to the selected active power distribution network, inputting line parameters, load levels, network topology connection relations, access positions and capacities of a controllable/uncontrollable distributed power supply, an energy storage system and an intelligent soft switch, operation curves of the load and the uncontrollable distributed power supply, system fault positions and island duration, reference voltage and reference power initial values;
2) establishing a deterministic active power distribution network island division model according to the active power distribution network structure and parameters provided in the step 1), and obtaining a compact form of the active power distribution network island division model, wherein the compact form comprises the following steps: setting the maximum restoration active load capacity of an active power distribution network within a period of time as an objective function, and respectively considering island generation constraint, power distribution network operation constraint, controllable/uncontrollable distributed power supply operation constraint, energy storage system operation constraint and intelligent soft switch operation constraint;
the compact form of the island division model of the active power distribution network is expressed as
Figure FDA0002780042220000011
s.t.Ax+Dy≥f
Bx+Cy＝d0
‖Hx+Gy‖2≤hTx+gTy
In the formula, variable
Figure FDA0002780042220000012
Representing the operation strategies of the controllable/uncontrollable distributed power supply, the energy storage system and the intelligent soft switch;
Figure FDA0002780042220000018
respectively the active power and the reactive power injected by the controllable distributed power supply on the node i in the t period;
Figure FDA0002780042220000016
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the node i in the t period;
Figure FDA0002780042220000013
the charging and discharging power of the energy storage system at the node i in the t period is positive, and the charging power is negative;
Figure FDA0002780042220000014
injecting reactive power into the energy storage system at a node i in the t period;
Figure FDA0002780042220000017
Figure FDA0002780042220000015
active power and reactive power injected by intelligent soft switches connected to the nodes i and j in the time period t respectively; the variable y: is ═ U (U)t,i,It,ji,Pt,ji,Qt,ji,Pt,i,Qt,i)TRepresenting a power flow control variable; pt,ji、Qt,jiRespectively the active power flowing through branch ji at t time intervalPower and reactive power; pt,i、Qt,iRespectively the sum of active power and reactive power injected into the node i in the t period; i ist,jiThe amplitude of the current flowing through the branch ji at the time t; u shapet,iThe voltage amplitude of the node i is t time period; the symbol A, B, C, D, H, G is the coefficient matrix of the model, and b, c, f, g, h are the coefficient vectors of the model;
Figure FDA0002780042220000019
representing the active power contribution prediction value of the uncontrollable distributed power supply, wherein,
Figure FDA00027800422200000110
the active power output predicted value of the uncontrollable distributed power supply on the node i in the t period is obtained;
3) setting an uncertain set of the uncontrollable distributed power sources according to the compact form of the active power distribution network island division model obtained in the step 2), and establishing an active power distribution network island division robust model considering the uncertainty of the distributed power sources; wherein the content of the first and second substances,
the uncertain set of the uncontrollable distributed power supply is expressed as
Figure FDA00027800422200000111
In the formula (I), the compound is shown in the specification,
Figure FDA00027800422200000112
representing a set of uncertainties for the uncontrollable distributed power supply; d represents the uncertainty active power output of the uncontrollable distributed power supply;
Figure FDA00027800422200000113
active power injected into the uncontrollable distributed power supply on the node i in the t period;
Figure FDA00027800422200000114
the active output predicted value of the uncontrollable distributed power supply on the node i in the t period is obtained; omeganThe method comprises the steps of collecting all nodes of a source power distribution network;
Figure FDA0002780042220000021
the fluctuation deviation of the active power output of the uncontrollable distributed power supply on a node i in the t period is obtained; gamma-shapedNDGAdjusting parameters for uncertainty of the uncontrollable distributed power supply;
the island division robust model of the active power distribution network considering the uncertainty of the distributed power supply comprises the following steps:
Figure FDA0002780042220000022
Figure FDA0002780042220000023
wherein
Figure FDA0002780042220000024
Figure FDA0002780042220000025
In the formula, variable
Figure FDA0002780042220000026
Representing the operation strategies of the controllable/uncontrollable distributed power supply, the energy storage system and the intelligent soft switch;
Figure FDA0002780042220000027
respectively the active power and the reactive power injected by the controllable distributed power supply on the node i in the t period;
Figure FDA0002780042220000028
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the node i in the t period;
Figure FDA00027800422200000210
the charging and discharging power of the energy storage system at the node i in the t period is positive, and the charging power is negative;
Figure FDA00027800422200000211
injecting reactive power into the energy storage system at a node i in the t period;
Figure FDA0002780042220000029
Figure FDA00027800422200000212
active power and reactive power injected by intelligent soft switches connected to the nodes i and j in the time period t respectively; the variable y: is ═ U (U)t,i,It,ji,Pt,ji,Qt,ji,Pt,i,Qt,i)TRepresenting a power flow control variable; pt,ji、Qt,jiRespectively the active power and the reactive power flowing through the branch ji at the time period t; pt,i、Qt,iRespectively the sum of active power and reactive power injected into the node i in the t period; i ist,jiThe amplitude of the current flowing through the branch ji at the time t; u shapet,iThe voltage amplitude of the node i is t time period; the symbol A, B, C, D, H, G is a coefficient matrix of the model, and c, f, g, h are coefficient vectors of the model; d represents the uncertainty active power output of the uncontrollable distributed power supply;
4) solving the active power distribution network island division robust model obtained in the step 3) by adopting a column and constraint generation algorithm;
5) and outputting the solving result of the step 4), including the recovery load coefficient of each node, the output power and the control mode of the controllable/uncontrollable distributed power supply, the charging and discharging power and the charge state of the energy storage system and the transmission power of the intelligent soft switch.
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